Navigating the Future: Intersection of Safety, Efficiency, and Resilience in Autonomous Traffic Systems

Sammanfattning: This thesis embarks on a journey in the advancement of urban traffic management, centering around the innovative integration of Autonomous Intersection Management (AIM) systems. The research encompasses a comprehensive exploration of various facets of AIM implementation, significantly contributing to the evolution of a more efficient and safer urban transport system.The research investigates the dynamic and complex environment of city transportation, addressing the myriad of challenges and opportunities that arise with the advancement of autonomous vehicle technology. It synthesizes a broad spectrum of dimensions in AIM implementation, collectively contributing to the vision of a more streamlined and safer urban transportation network.A pivotal aspect of this thesis is the exploration of the interplay between autonomous and non-autonomous vehicles in urban settings. The study assesses the robustness and resilience of AIM systems across diverse and unpredictable scenarios, with a focus on adaptive control strategies, wireless communication challenges, and efficient traffic flow management. This emphasis highlights the crucial role of these systems in ensuring safety and efficiency, especially in mixed-traffic environments.A notable contribution of this work is the integration of cutting-edge technologies like machine learning with AIM, proposing innovative solutions for traffic management. These solutions are designed to reduce operational complexities and enhance scalability, showcasing potential to transform traditional traffic management practices. This aspect of the research not only demonstrates innovation but also practical applicability in urban contexts.The thesis culminates in demonstrating the practical application and effectiveness of AIM systems in real-world urban contexts. Besides underscoring the relevance and utility of the research, it also showcases the practical applicability of the model. The findings and developments presented in this work pave the way for future advancements in the field.In summary, this thesis offers a substantial contribution to urban transportation management by providing innovative insights and practical solutions for the integration and optimization of AIM systems. It lays a foundational framework for future research, steering towards advanced transportation networks, which are both safe and efficient.

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